Abstract 1360: Spatial image analysis of tumor-infiltrating lymphocytes on gastric cancer to predict anti-PD1 inhibitor response
Authors: Sanghoon Lee, Sunho Park, Jae-Ho Cheong, Sam C. Wang, Matthew R. Porembka, In-Ho Kim, Sung Hak Lee, Tae Hyun Hwang
Journal: Cancer Research (2023)

Abstract Background: The Tumor Immune microenvironment (TME) plays an important role in tumor development, progression, and treatment response. Recent studies show that spatial heterogeneity of Tumor-Infiltrating Lymphocytes (TILs), consisting of lymphocytic cell populations, is an important histopathologic feature and a powerful prognostic and predictive marker correlating with cancer progression and treatment response. Detecting and quantifying TIL density in Whole Slide Images (WSIs) is, however, labor-intensive, and it is difficult to assess differences in the composition of immune cells. Therefore, it remains a challenge to establish the analysis of TILs in the complex TME. We present an automated spatial image analysis of TILs on gastric cancer to address cancer immunotherapy. Method: 1,031WSIs of hematoxylin and eosin-stained (H&amp;E) formalin-fixed paraffin-embedded (FFPE) sections from four patient cohorts: TCGA-STAD (329), Yonsei-1 (620 patients), Yonsei-2 (45), and St. Mary (37), were analyzed. Two treatment groups in response were classified as CR + PR, while another two treatment groups in non-response were classified as PD + SD. H&amp;E stained WSIs were normalized using the Reinhard normalization method at 20X magnification for reducing the color variability of the stained tissue images. We used U-Net architecture to predict the normalized H&amp;E stained WSIs. Three U-Net models were trained for predicting tumor, TILs, and stroma, using Adam optimizer, 1e-4 learning rate, a binary loss function, L2 regularization, and 50 epochs. The normalized H&amp;E stained WSIs were predicted using the trained U-Net models. Spatial analysis of TILs was conducted on the predicted WSIs. Regional quantification of TILs was performed by two immune phenotype groups: immune-immersed (TILs enriched in tumor cells) and immune-excluded (TILs not enriched in tumor cells but in stromal cells). Kaplan-Meier with a log-rank test was used to evaluate the survival differences between the groups. Results: In TCGA-STAD, the proportion of immune-immersed composition is significantly different from the immune-excluded (p-value 0.05). In Yonsei-1, the immune-excluded composition is significantly different among the three risk groups (p-value 3e-05). In the Yonsei-2 and St. Mary cohorts, including anti-PD1 treated gastric cancer patients, the immune-immersed composition is significantly different between two response groups (p-value 0.007) and represented the diagnostic accuracy (AUROC: 0.78) on CR + PR groups. Conclusion: These findings suggest that the proposed TIL quantification in the complex TME is strongly associated with patient prognosis and anti-PD1 treatment response in gastric cancer. Citation Format: Sanghoon Lee, Sunho Park, Jae-Ho Cheong, Sam C. Wang, Matthew R. Porembka, In-Ho Kim, Sung Hak Lee, Tae Hyun Hwang. Spatial image analysis of tumor-infiltrating lymphocytes on gastric cancer to predict anti-PD1 inhibitor response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1360.